Engineering Commons^™

Reinventing Integrated Photonic Devices And Circuits For High Performance Communication And Computing Applications, Venkata Sai Praneeth Karempudi

Theses and Dissertations--Electrical and Computer Engineering

The long-standing technological pillars for computing systems evolution, namely Moore's law and Von Neumann architecture, are breaking down under the pressure of meeting the capacity and energy efficiency demands of computing and communication architectures that are designed to process modern data-centric applications related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In response, both industry and academia have turned to 'more-than-Moore' technologies for realizing hardware architectures for communication and computing. Fortunately, Silicon Photonics (SiPh) has emerged as one highly promising ‘more-than-Moore’ technology. Recent progress has enabled SiPh-based interconnects to outperform traditional electrical interconnects, offering advantages like high bandwidth density, …

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

Optimal Design Of Special High Torque Density Electric Machines Based On Electromagnetic Fea, Murat G. Kesgin

Theses and Dissertations--Electrical and Computer Engineering

Electric machines with high torque density are essential for many low-speed direct-drive systems, such as wind turbines, electric vehicles, and industrial automation. Permanent magnet (PM) machines that incorporate a magnetic gearing effect are particularly useful for these applications due to their potential for achieving extremely high torque density. However, when the number of rotor polarities is increased, there is a corresponding need to increase the number of stator slots and coils proportionally. This can result in manufacturing challenges. A new topology of an axial-flux vernier-type machine of MAGNUS type has been presented to address the mentioned limitation. These machines can …

Establishing The Foundation To Robotize Complex Welding Processes Through Learning From Human Welders Based On Deep Learning Techniques, Rui Yu

Theses and Dissertations--Electrical and Computer Engineering

As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF).

This …

Application Of Conventional Feedforward And Deep Neural Networks To Power Distribution System State Estimation And State Forecasting, James Paul Carmichael

Theses and Dissertations--Electrical and Computer Engineering

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn …

Building Energy Modeling And Studies Of Electric Power Distribution Systems With Distributed Energy Resources, Evan S. Jones

Theses and Dissertations--Electrical and Computer Engineering

There is significant opportunity for savings in energy and investment from improved performance of electric Power Distribution Systems (PDSs) through optimal planning and operation of conventional voltage-controlling devices. Novel multi-step model conversion and optimal capacitor planning (OCP) procedures are proposed for large-scale utility PDSs and are exemplified with an existing utility circuit of approximately 4,000 buses. Simulated optimal control and operation is achieved with a cluster-based approach that utilizes load-forecasting to minimize equipment degradation by intelligently dispersing device setting adjustments over time such that they remain most applicable. Improved performance may also be achieved through smart building technologies and Virtual …

A Phase Change Memory And Dram Based Framework For Energy-Efficient And High-Speed In-Memory Stochastic Computing, Supreeth Mysore

Theses and Dissertations--Electrical and Computer Engineering

Convolutional Neural Networks (CNNs) have proven to be highly effective in various fields related to Artificial Intelligence (AI) and Machine Learning (ML). However, the significant computational and memory requirements of CNNs make their processing highly compute and memory-intensive. In particular, the multiply-accumulate (MAC) operation, which is a fundamental building block of CNNs, requires enormous arithmetic operations. As the input dataset size increases, the traditional processor-centric von-Neumann computing architecture becomes ill-suited for CNN-based applications. This results in exponentially higher latency and energy costs, making the processing of CNNs highly challenging.

To overcome these challenges, researchers have explored the Processing-In Memory (PIM) …

Modeling The Early Visual System, Nicholas Lanning

Theses and Dissertations--Electrical and Computer Engineering

There are two encoding schema present in simple cells in the early visual system of vertebrates: the retinal simple cells activate highly when the receptive field contains a center surround stimulus, while the primary visual cortex’s (V1) simple cells activate highly when the receptive field contains visual edges. Work has been done in the past to enforce constraints on visual machine learning such that the retinal or V1 encoding is learned, but this work is often done to emulate retinal and V1 encoding in a vacuum. Recent work using convolutional neural networks focuses on anatomical constraints along with a supervised …

Determining Power System Fault Location Using Neural Network Approach, Edward O. Ojini

Theses and Dissertations--Electrical and Computer Engineering

Fault location remains an extremely pivotal feature of the electric power grid as it ensures efficient operation of the grid and prevents large downtimes during fault occurrences. This will ultimately enhance and increase the reliability of the system. Since the invention of the electric grid, many approaches to fault location have been studied and documented. These approaches are still effective and are implemented in present times, and as the power grid becomes even more broadened with new forms of energy generation, transmission, and distribution technologies, continued study on these methods is necessary. This thesis will focus on adopting the artificial …

Development Of Dc Circuit Breakers For Medium-Voltage Electrified Transportation, Trevor Morgan Arvin

Theses and Dissertations--Electrical and Computer Engineering

Medium-voltage DC (MVDC) distribution is an enabling technology for the electrification of transportation such as aircraft and shipboard. One main obstacle for DC distribution is the lack of adequate circuit fault protection. The challenges are due to the rapidly rising fault currents and absence of zero crossings in DC systems compared to AC counterparts. Existing DC breaker solutions lack comprehensive consideration of energy efficiency, power density, fault interruption speed, reliability, and implementation cost.

In this thesis, two circuit topologies of improved DC circuit breakers are developed: the resonant current source based hybrid DC breaker (RCS-HDCB) and the high temperature superconductor …

Synthesizing Dysarthric Speech Using Multi-Speaker Tts For Dsyarthric Speech Recognition, Mohammad Soleymanpour

Theses and Dissertations--Electrical and Computer Engineering

Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech is required, which is not readily available for dysarthric talkers.

In this dissertation, we investigate dysarthric speech augmentation and synthesis methods. To better understand differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels, a comparative study between typical and dysarthric speech was conducted. These characteristics are important components for dysarthric speech modeling, …

Hourly Dispatching Wind-Solar Hybrid Power System With Battery-Supercapacitor Hybrid Energy Storage, Pranoy Kumar Singha Roy

Theses and Dissertations--Electrical and Computer Engineering

This dissertation demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a specific dispatching horizon for an entire day utilizing a hybrid energy storage system (HESS) configured by batteries and supercapacitors. Here, wind speed and solar irradiance are predicted one hour ahead of time using a multilayer perceptron Artificial Neural Network (ANN), which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, multiple state of charge (SOC) controllers as a function of energy storage system (ESS) SOC are developed to accurately estimate the grid reference power (P_Grid,ref) for each dispatching period. …

Parametric Average-Value Modeling, Simulation, And Characterization Of Machine-Rectifier Systems, Isuje Ojo

Theses and Dissertations--Electrical and Computer Engineering

There are many techniques for modeling and simulation of synchronous machine-rectifier systems. The more common approaches are the detailed and average-value modeling techniques. The detailed simulation technique takes into account the details of the diode switching and is both very accurate and very expensive in terms of computational resources. To alleviate this disadvantage, the average-value modeling technique is often utilized. In this approach, the details of diode switching are neglected or averaged. In that light, the work presented herein proposes a unique saliency-sensitive parametric average-value model (SSPAVM) of the synchronous machine-rectifier system. This model extends existing parametric average-value models to …

Energy-Efficient And Secure Hardware Using Adiabatic Logic And Non-Volatile Mtj Devices, Zachary Kahleifeh

Theses and Dissertations--Electrical and Computer Engineering

Internet of Things (IoT) is a collection of devices that exchange data through a network to implement complex applications. IoT devices increase the quality of life of their user base which has a wide variety such as the medical field, consumer electronics, and the manufacturing sector. However, IoT devices have several challenges that need to be overcome namely, security and energy consumption. The threat vector that IoT devices face is growing and includes the following threats, the leakage of information through a side-channel attack known as the Correlation Power Analysis (CPA), authentication, piracy, etc. A side-channel attack is an attack …

Models And Optimal Controls For Smart Homes And Their Integration Into The Electric Power Grid, Huangjie Gong

Theses and Dissertations--Electrical and Computer Engineering

Smart homes can operate as a distributed energy resource (DER), when equipped with controllable high-efficiency appliances, solar photovoltaic (PV) generators, electric vehicles (EV) and energy storage systems (ESS). The high penetration of such buildings changes the typical electric power load profile, which without appropriate controls, may become a “duck curve” when the surplus PV generation is high, or a “dragon curve” when the EV charging load is high. A smart home may contribute to an optimal solution of such problems through the energy storage capacity, provided by its by battery energy storage system (BESS), heating, ventilation, and air conditioning (HVAC) …

Explainable Data-Driven Motor Condition Monitoring And Fault Disgnosis, Yuming Wang

Theses and Dissertations--Electrical and Computer Engineering

Industrial motors are widely used in various fields such as power generation, mining, and manufacturing. Motor faults and time-consuming maintenance process will lead to serious economic losses in this context. To monitor motor faults and detect motor conditions, different types of sensors that can test vibration and current signals are mounted on motors. However, the main challenge was how to use information gained by sensors to analyze or diagnose motor conditions.

Machine learning is a popular technology in recent years, and it's very suitable for crunching and analyzing data. As an important subset of machine learning, deep learning is suitable …

Three Dimensional Photonics Structures: Design And Applications, Mansoor Sultan

Theses and Dissertations--Electrical and Computer Engineering

Photonics is an emerging technology for light control, emission, and detection. Photonic devices control photons the same way electronic circuits control electrons in active or passive mode depending on the energy requirement of the device. This dissertation will discuss the design, fabrication, testing of photonic structures with applications including imaging and renewable energy. First, we developed a novel lithography method for fluoropolymer resist based on variable pressure electron beam lithography (VP-EBL). VP-EBL proves to be an efficient method for patterning a widely used, but challenging to process, fluoropolymer, Teflon AF. However, rather than solely mitigating charging, the ambient gas is …

Novel Machine Learning And Wearable Sensor Based Solutions For Smart Healthcare Monitoring, Rajdeep Kumar Nath

Theses and Dissertations--Electrical and Computer Engineering

The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These health monitoring systems can be utilized for monitoring the mental and physical wellbeing of a person. Stress, anxiety, and hypertension are the major elements responsible for the plethora of physical and mental illnesses. In this context, the older population demands special attention because of the several age-related complications that exacerbate the effects of stress, anxiety, and hypertension. Monitoring stress, anxiety, and blood pressure regularly can prevent long-term damage by initiating necessary intervention or clinical treatment beforehand. This will improve the quality of life …

Weakly Supervised Learning For Multi-Image Synthesis, Muhammad Usman Rafique

Theses and Dissertations--Electrical and Computer Engineering

Machine learning-based approaches have been achieving state-of-the-art results on many computer vision tasks. While deep learning and convolutional networks have been incredibly popular, these approaches come at the expense of huge amounts of labeled data required for training. Manually annotating large amounts of data, often millions of images in a single dataset, is costly and time consuming. To deal with the problem of data annotation, the research community has been exploring approaches that require less amount of labelled data.

The central problem that we consider in this research is image synthesis without any manual labeling. Image synthesis is a classic …

Accelerometer-Based Vigilance State Classification In Dairy Cows, Evan King

Theses and Dissertations--Electrical and Computer Engineering

Globally, dairy farming is a $700 billion industry, with more than 9 million dairy cows in the United States alone. Depriving cows of required activities such as sleep has been shown to negatively impact reproductive efficiency, decrease the volume of milk produced, and increase the risk of culling. Overcrowded herds can decrease individual animal health, demanding the need for automatic behavior detection that would provide insight into their state of health.

Using electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) to characterize the phases of sleep is a technique which has been used for decades. While these techniques are considered the …

Research On Power System State Estimation Problems – Series-Compensated Transmission Line Parameter And Load Model Parameter Estimation, Yiqi Zhang

Theses and Dissertations--Electrical and Computer Engineering

Transmission line and load model parameters are essential inputs to power system modeling and simulation, control, protection, operation, optimization, and planning. These parameters usually vary over time or under different operating conditions. Thus, reliable estimation methods are desired to ensure the accuracy of those parameters. This research focuses on estimation for transmission line parameters and the ZIP load model. The proposed estimation methods can use both online measurements and historical data of a specified duration. The parameters of long transmission lines with different series-compensation configurations are estimated using linear methods and optimal estimators with bad data detection capability. Additionally, Kalman …

Re-Designing Main Memory Subsystems With Emerging Monolithic 3d (M3d) Integration And Phase Change Memory Technologies, Chao-Hsuan Huang

Theses and Dissertations--Electrical and Computer Engineering

Over the past two decades, Dynamic Random-Access Memory (DRAM) has emerged as the dominant technology for implementing the main memory subsystems of all types of computing systems. However, inferring from several recent trends, computer architects in both the industry and academia have widely accepted that the density (memory capacity per chip area) and latency of DRAM based main memory subsystems cannot sufficiently scale in the future to meet the requirements of future data-centric workloads related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In fact, the achievable density and access latency in main memory subsystems presents a very fundamental …

Transmission-Level Impact Analysis Of Utility-Scale Solar Photovoltaic Systems And Battery Energy Storage Grid Support, Gerald W. Bankes Ii

Theses and Dissertations--Electrical and Computer Engineering

Solar photovoltaic energy generation is expected to grow dramatically in coming years in order to take advantage of renewable and clean sources of electricity. This thesis presents research on the impact of increasing solar PV penetration, specifically of large, utility-scale PV facilities, on transmission network performance. The development of Python programming tools for automation of power flow analysis is presented. A modified version of the IEEE 118-Bus test system is developed and modified to simulate increasing PV generation on the transmission system. The impacts on performance are analyzed trends are reported. Battery energy storage systems are studied in this thesis …

Designing Novel Hardware Security Primitives For Smart Computing Devices, Amitkumar Degada

Theses and Dissertations--Electrical and Computer Engineering

Smart computing devices are miniaturized electronics devices that can sense their surroundings, communicate, and share information autonomously with other devices to work cohesively. Smart devices have played a major role in improving quality of the life and boosting the global economy. They are ubiquitously present, smart home, smart city, smart girds, industry, healthcare, controlling the hazardous environment, and military, etc. However, we have witnessed an exponential rise in potential threat vectors and physical attacks in recent years. The conventional software-based security approaches are not suitable in the smart computing device, therefore, hardware-enabled security solutions have emerged as an attractive choice. …

Development Of A Hybrid-Electric Aircraft Propulsion System Based On Silicon Carbide Triple Active Bridge Multiport Power Converter, Cole M. Ivey

Theses and Dissertations--Electrical and Computer Engineering

Constrained by the low energy density of Lithium-ion batteries with all-electric aircraft propulsion, hybrid-electric aircraft propulsion drive becomes one of the most promising technologies in aviation electrification, especially for wide-body airplanes. In this thesis, a three-port triple active bridge (TAB) DC-DC converter is developed to manage the power flow between the turbo generator, battery, and the propulsion motor. The TAB converter is modeled based on the emerging Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) modules operating at high switching frequency, so the size of the magnetic transformer can be significantly reduced. Different operation modes of this hybrid-electric propulsion drive …

Special Power Electronics Converters And Machine Drives With Wide Band-Gap Devices, Yibin Zhang

Theses and Dissertations--Electrical and Computer Engineering

Power electronic converters play a key role in power generation, storage, and consumption. The major portion of power losses in the converters is dissipated in the semiconductor switching devices. In recent years, new power semiconductors based on wide band-gap (WBG) devices have been increasingly developed and employed in terms of promising merits including the lower on-state resistance, lower turn-on/off energy, higher capable switching frequency, higher temperature tolerance than conventional Si devices. However, WBG devices also brought new challenges including lower fault tolerance, higher system cost, gate driver challenges, and high dv/dt and resulting increased bearing current in electric machines.

This …

Cross Approximation Methods For Integral Equation Matrices With Complex Structure, Jordon N. Blackburn

Theses and Dissertations--Electrical and Computer Engineering

Electrical and computer engineers rely on electromagnetic field (EM) theory to formulate and design systems that utilize information or energy obtained from a signal. Over time these systems have been increased in scale and complexity and adapted to handle a wider array of problems. This has motivated substantial developments in computational sciences including the area of computational electromagnetics (CEM).The focus of CEM is the simulation of electromagnetic fields. At the University of Kentucky, the CEM group has developed several modeling tools that are based on the application of approximation theory to integral equations. This allows the physical problem to be …

Toward Intelligent Welding By Building Its Digital Twin, Qiyue Wang

Theses and Dissertations--Electrical and Computer Engineering

To meet the increasing requirements for production on individualization, efficiency and quality, traditional manufacturing processes are evolving to smart manufacturing with the support from the information technology advancements including cyber-physical systems (CPS), Internet of Things (IoT), big industrial data, and artificial intelligence (AI). The pre-requirement for integrating with these advanced information technologies is to digitalize manufacturing processes such that they can be analyzed, controlled, and interacted with other digitalized components. Digital twin is developed as a general framework to do that by building the digital replicas for the physical entities. This work takes welding manufacturing as the case study to …

Electric Power Systems And Components For Electric Aircraft, Damien Lawhorn

Theses and Dissertations--Electrical and Computer Engineering

Electric aircraft have gained increasing attention in recent years due to their potential for environmental and economic benefits over conventional airplanes. In order to offer competitive flight times and payload capabilities, electric aircraft power systems (EAPS) must exhibit extremely high efficiencies and power densities. While advancements in enabling technologies have progressed the development of high performance EAPS, further research is required.

One challenge in the design of EAPS is determining the best topology to be employed. This work proposes a new graph theory based method for the optimal design of EAPS. This method takes into account data surveyed from a …

Boundary Integral Equation Method For Electrostatic Field Prediction In Piecewise-Homogeneous Electrolytes, Christopher Keith Pratt

Theses and Dissertations--Electrical and Computer Engineering

This thesis presents a method to predict electrostatic fields, potentials, and currents in regions containing piecewise-homogeneous electrolytes. Additionally, an efficient electric field calculation is presented. A boundary integral equation is formulated for the boundary potentials and currents and is discretized using the Locally Corrected Nyström method. Solution convergence with respect to the mesh discretization and basis order is investigated. The techniques are validated through analysis of problems with either analytic solutions, with published data, or with other solution methods.